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» Ensemble learning for free with evolutionary algorithms
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CEC
2010
IEEE
13 years 2 months ago
Differential evolution with ensemble of constraint handling techniques for solving CEC 2010 benchmark problems
Several constraint handling techniques have been proposed to be used with the evolutionary algorithms (EAs). According to the no free lunch theorem, it is impossible for a single c...
Rammohan Mallipeddi, Ponnuthurai Nagaratnam Sugant...
AUSAI
2005
Springer
13 years 7 months ago
A Comparison of Evolutionary Methods for the Discovery of Local Search Heuristics
Abstract. Methods of adaptive constraint satisfaction have recently become of interest to overcome the limitations imposed on “black-box” search algorithms by the no free lunch...
Stuart Bain, John Thornton, Abdul Sattar
GECCO
2009
Springer
128views Optimization» more  GECCO 2009»
14 years 2 days ago
Neural network ensembles for time series forecasting
This work provides an analysis of using the evolutionary algorithm EPNet to create ensembles of artificial neural networks to solve a range of forecasting tasks. Several previous...
Victor M. Landassuri-Moreno, John A. Bullinaria
GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
13 years 11 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen
NN
2008
Springer
146views Neural Networks» more  NN 2008»
13 years 5 months ago
Clustering and co-evolution to construct neural network ensembles: An experimental study
This paper introduces an approach called Clustering and Co-evolution to Construct Neural Network Ensembles (CONE). This approach creates neural network ensembles in an innovative ...
Fernanda L. Minku, Teresa Bernarda Ludermir